{"id":"W4386824661","doi":"10.1021/acs.analchem.3c02477","title":"Quantitative Comparison of Capture-SELEX, GO-SELEX, and Gold-SELEX for Enrichment of Aptamers","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Chemistry; DNA; SELEX Aptamer Technique; Computational biology; Molecular biology; Biology; Biochemistry; RNA; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002804625,0.001676126,0.001071578,0.001752721,0.0003335928,0.001025522,0.0007522606,0.0008528547,0.001210228],"category_scores_gemma":[0.00318711,0.0003947491,0.0006896657,0.0009785285,0.0006766278,0.000576164,0.001040947,0.0008421544,0.0005650013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006405166,"about_ca_system_score_gemma":0.0003035221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008113933,"about_ca_topic_score_gemma":0.001288373,"domain_scores_codex":[0.9965364,0.0006826234,0.0002372035,0.0005710855,0.001731081,0.0002415633],"domain_scores_gemma":[0.9982941,0.0009082052,0.0001472231,0.0001279763,0.0004508247,0.00007179245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001635443,0.0001003365,0.001044419,0.0005199314,0.0001262164,0.00006891221,0.0001241593,0.001892347,0.9795542,0.0002931881,0.0001932016,0.01591953],"study_design_scores_gemma":[0.000008115167,0.0001721922,0.001788673,0.00001667695,0.00005841571,0.0001036646,0.00003460185,0.004471861,0.9918671,0.0001083381,0.00134702,0.00002333009],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7368163,0.008304295,0.2443962,0.0002914373,0.0001626004,0.0007954098,0.001547355,0.001428637,0.006257639],"genre_scores_gemma":[0.7242088,0.005579959,0.2491336,0.0004851088,0.00006226859,0.001509447,0.004268038,0.0004084208,0.01434451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002804625,"threshold_uncertainty_score":0.01483244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02624038765871082,"score_gpt":0.3501363489285115,"score_spread":0.3238959612698006,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}